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Machine learning approach to radio frequency interference(RFI) classification in Radio Astronomy
Thesis (MEng)--Stellenbosch University, 2016.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2016
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| _version_ | 1867613955921281024 |
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| access_status_str | Open Access |
| author | Wolfaardt, Cornelis Johannes |
| author2 | Niesler, T. R. |
| author_browse | Niesler, T. R. Wolfaardt, Cornelis Johannes |
| author_facet | Niesler, T. R. Wolfaardt, Cornelis Johannes |
| author_sort | Wolfaardt, Cornelis Johannes |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MEng)--Stellenbosch University, 2016. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/98464 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:44:22.712Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/98464 Machine learning approach to radio frequency interference(RFI) classification in Radio Astronomy Wolfaardt, Cornelis Johannes Niesler, T. R. Davidson, D. B. Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. Radio astronomy UCTD Radio -- Interference Electromagnetic interference Radio -- Transmitters and transmission Radio -- Antennas Radio - Recievers and reception Radio frequency interference Thesis (MEng)--Stellenbosch University, 2016. ENGLISH ABSTRACT: Radio frequency interference (RFI) presents a large problem for radio telescopes. Interference prevents observations from being made, or extends the duration required for observations. This thesis investigates different methods to automatically classify RFI signals. Data from different sources was cap- tured at the SKA site. Both Gaussian Mixture Model (GMM) and K-nearest neighbors (KNN) classifiers were used to analyse the data. Both performed adequately, with the KNN slightly outperforming the GMM. Different feature extraction methods were also investigated. AFRIKAANSE OPSOMMING: Radio frekwensie steurseine verteenwoordig `n groot probleem vir radio tele- skope. Steurseine verhoed teleskope om waarnemings te maak. Hierdie tesis ondersoek verskeie metodes om steurseine automaties te identifiseer en klasi- fiseer. Data van bekende steurseine op die SKA terrein is versamel. Verkeie voorverwerkingtegnieke word ondersoek en dan geannaliseer met bekende sta- tistiese modelle soos `n GMM en KNN. Beide lewer aanvaarbare resultate. Verskeie metodes om kenmerke te onttrek word ook ondersoek. 2016-03-09T14:22:12Z 2016-03-09T14:22:12Z 2016-03 Thesis http://hdl.handle.net/10019.1/98464 en_ZA Stellenbosch University xii, 75 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Radio astronomy UCTD Radio -- Interference Electromagnetic interference Radio -- Transmitters and transmission Radio -- Antennas Radio - Recievers and reception Radio frequency interference Wolfaardt, Cornelis Johannes Machine learning approach to radio frequency interference(RFI) classification in Radio Astronomy |
| title | Machine learning approach to radio frequency interference(RFI) classification in Radio Astronomy |
| title_full | Machine learning approach to radio frequency interference(RFI) classification in Radio Astronomy |
| title_fullStr | Machine learning approach to radio frequency interference(RFI) classification in Radio Astronomy |
| title_full_unstemmed | Machine learning approach to radio frequency interference(RFI) classification in Radio Astronomy |
| title_short | Machine learning approach to radio frequency interference(RFI) classification in Radio Astronomy |
| title_sort | machine learning approach to radio frequency interference rfi classification in radio astronomy |
| topic | Radio astronomy UCTD Radio -- Interference Electromagnetic interference Radio -- Transmitters and transmission Radio -- Antennas Radio - Recievers and reception Radio frequency interference |
| url | http://hdl.handle.net/10019.1/98464 |
| work_keys_str_mv | AT wolfaardtcornelisjohannes machinelearningapproachtoradiofrequencyinterferencerficlassificationinradioastronomy |